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Metabolomic Profiling of Arginine Metabolome Links Altered Methylation to Chronic Kidney Disease Accelerated Atherosclerosis
Anna V Mathew1,#, Lixia Zeng1,#, Jaeman Byun1, and Subramaniam Pennathur1,2,*
1Division of Nephrology, Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
2Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA
Abstract
Atherosclerotic cardiovascular disease is the leading cause of death in patients with chronic kidney
disease (CKD), but the mechanisms underlying vascular disease has not been fully understood. As
the nitrogen donor in nitric oxide (NO·) synthesis, arginine and its metabolic products are
integrally linked to vascular health and information. We hypothesized that derangements in this
pathway could explain, in part, increased atherosclerotic risk in CKD. We developed a targeted
metabolomic platform to profile quantitatively arginine metabolites in plasma by liquid
chromatography tandem mass spectrometry (LC/MS). Male low-density lipoprotein receptor
defcient (LDLr−/−) mice at age 6 weeks were subjected to sham or 5/6 nephrectomy surgery to
induce CKD. Subsequently, the animals were maintained on high fat diet for 24 weeks. Targeted
metabolomic analysis of arginine metabolites in plasma was performed by isotope dilution LC/MS
including asymmetric dimethyl arginine (ADMA), symmetric dimethyl arginine (SDMA), N-
mono-methylarginine (NMMA), arginine and citrulline. Although elevated plasma levels of
ADMA and SDMA were found in the CKD mice, only higher ADMA level correlated with degree
of atherosclerosis. No significant differences were noted in levels of NMMA between the groups.
CKD mice had high levels of citrulline and arginine, but ADMA levels had no correlation with
either of these metabolites. These fndings strongly implicate altered arginine methylation and
accumulation of ADMA, may in part contribute to CKD accelerated atherosclerosis. It raises the
possibility that interrupting pathways that generate ADMA or enhance its metabolism may have
therapeutic potential in mitigating atherosclerosis.
Keywords
Chronic kidney disease; Animal model; Arginine methylation; Atherosclerosis; Asymmetric dimethyl arginine
This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.*Corresponding author: Subramaniam Pennathur, University of Michigan, 5309 Brehm Center, 1000 Wall St., Ann Arbor, MI 48105, USA, Tel: (734) 764-3269; Fax: (734) 232-8175; [email protected].#These authors contributed equally to the work
HHS Public AccessAuthor manuscriptJ Proteomics Bioinform. Author manuscript; available in PMC 2016 January 14.
Published in final edited form as:J Proteomics Bioinform. 2015 October ; Suppl 14: . doi:10.4172/jpb.S14-001.
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Introduction
Coronary artery disease (CAD) and cardiovascular (CV) events associates with chronic
kidney disease (CKD) and is the leading cause of death in patients with CKD (>10-fold
mortality). Indeed, CV events and mortality are more likely outcome than progression to
end-stage renal disease (ESRD) in CKD subjects [1–6]. Cross-sectional studies have
demonstrated that the traditional risk factors are only partially predictive of CAD in CKD
subjects, implying the presence of additional CKD- specific risk factors [2]. In response to
physiologic stimuli, endothelial cells dynamically regulate arterial vascular tone by
producing vasodilators and vasoconstrictors. Risk factors for atherosclerosis, such as CKD,
interfere with this response, promoting endothelial dysfunction and atherosclerosis. One key
regulator is nitric oxide (NO), which is generated from L-arginine by endothelial nitric oxide
synthase (eNOS) in the presence of cofactors such as tetrahydrobiopterin. Gaseous NO
diffuses to vascular smooth muscle cells and activates guanylate cyclase, which in turn
elevates cyclic guanosine monophosphate to promote vasodilation. NO’s antithrombotic
nature prevents platelet aggregation, promotes fibrinolysis and decreases smooth muscle
proliferation [7].
Asymmetric dimethylarginine (ADMA: NGNGdimethylarginine) is the naturally occurring
dimethylated modification of arginine and is a known inhibitor of NOS. One pathway for
producing ADMA is proteolysis of methylated proteins which are formed by protein
arginine methyl transferases (PRMT) (Figure 1). Once released into plasma, ADMA can
inhibit eNOS and decrease NO bioavailability, causing endothelial dysfunction. The enzyme
dimethyl arginine dimethyl amino hydrolase (DDAH) metabolizes ADMA to generate
dimethylamine and citrulline [8–11]. DDAH has two isoforms of which DDAH1 is thought
be the primary enzyme responsible for ADMA degradation [12–14]. While the proximal
tubules of kidneys can reabsorb almost all of the filtered L-arginine, very little ADMA is
reabsorbed or excreted into urine. The majority of filtered ADMA is degraded into citrulline
and dimethylamine by the renal DDAH as the kidneys have abundant amount of DDAH1.
Thus in CKD, loss of DDAH1 activity may limit ADMA breakdown [15]. Symmetric
dimethyl arginine (SDMA; NGN′Gdimethylarginine), a stereo somer of ADMA is also
produced by proteolysis following PRMT methylation of protein-bound arginine, but has no
NOS inhibitory activity and is renally excreted. NGmonomethyl-L-arginine (NMMA) is the
precursor of both ADMA and SDMA and is a more potent but less abundant NOS inhibitor.
The effect of these methylation products on hypertension and cardiovascular morbidity in
both CKD and healthy populations is well established [9,16–20]. Human studies have
demonstrated that plasma ADMA levels are increased in CKD, hypertension, diabetes,
obesity and metabolic syndrome [21–25] and are able to predict CV events [26,27]. Higher
ADMA levels also predict cardiovascular events in dialysis patients [28,29], CAD related
mortality in pre-dialysis CKD patients [30–32] and associates with CKD progression
[18,33]. ADMA is known to promote renal collagen and TGF-β production, thus promoting
renal fibrosis and partly explaining the association with CKD progression [34]. While these
associative studies raise the possibility that altered arginine metabolism may contribute to
atherosclerosis in CKD, a direct link between degree of atherosclerosis and the arginine
metabolome has not been previously tested in experimental models of CKD atherosclerosis.
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In this study, we demonstrate altered arginine methylation is linked to degree of
atherosclerosis in a CKD mouse model, raising the possibility of a therapeutic potential of
interrupting this pathway in CKD-atherosclerosis.
Methods
Reagents and materials
Male C57BL/6 LDLr−/− mice were purchased from Jackson Labs, Bar Harbor, ME.
Authentic and isotopically labeled standards were purchased from the following vendors:
NMMA, SDMA and D6 SDMA (Santa Cruz, CA); D7 ADMA (Novachem, Australia);
Creatinine, D3 Creatinine, Arginine and Citrulline (Sigma-Aldrich); 13C5-Citrulline
and 13C6-Arginine (Cambridge Isotope Laboratories, MA). All LC reagents were purchased
from Sigma Aldrich, St. Louis, MO. Rodent diets were purchased from LabDiet® and high
fat diet from Harlan Teklad.
Mouse models
All animal procedures were approved by the University of Michigan Committee on Use and
Care of Animals. Six week old male C57BL/6 LDLr−/− mice were fed LabDiet® standard
rodent diet that has 200 ppm cholesterol, 28.5% protein, 13.5% fat and 58.0% carbohydrates
by calories. Mice were housed in a climate-controlled, light-regulated facility with a 12:12
hour light-dark cycle and water ad libitum. At age 7 weeks, mice were subjected to either to
sham surgery (Control, n=11) or to 5/6 nephrectomy to induce CKD (CKD, n =11). This
was accomplished by removing entire right kidney in a first procedure and then subsequent
removal of two thirds left kidney by dissection after one week interval. At 9 weeks of age,
mice in each group were fed on high fat diet containing 19.5% protein, 40.5% fat, 0.5%
cholesterol and 40.0% carbohydrates from Harlan Teklad (TD00243). Murine systolic blood
pressure was measured by the IITC Life Science blood pressure system (Woodland Hills,
CA) with a highly sensitive photoelectric sensor. The accuracy of measurement was
confirmed with five or more successful readings obtained and secured by regular calibration
of the pressure transducer. Blood was collected from saphenous veins in living mice with
tubes containing dry ethylenediaminetetraacetate (EDTA). Hematocrit was measured by
CritSpin® Micro-Hematocrit centrifuge with Digital Hematocrit Reader (StatSpin®
Company).
Analysis of kidney function
Plasma creatinine levels were measured by liquid chromatography electron spray ionization
and tandem mass spectrometry (LC/ESI/MS) using an Agilent 6410 MS coupled with 1200
LC system (Agilent, New Castle, DE), as described previously [35]. Briefly, 5 μl of plasma
(n=11, each group) and 5 μl of deuterated creatinine (10 pmol/μl) were added into 90 μl of
20 mM ammonium acetate solution, subjected to protein precipitation by 85% acetonitrile. 2
μl of the supernatant was then injected for LC/MS analysis. A hydrophilic interaction
chromatography (HILIC) was performed utilizing Luna Phenomenex column (2.1 × 150mm,
3μm; Torrance, CA)) with an isocratic gradient of 85% acetonitrile with 20 mM ammonium
acetate for 5 min at flow rate of 0.3 mL/min. The ion transition of the m/z 114 to m/z 44 for
creatinine and m/z 117 to m/z 47 for D3-creatinine was monitored in the multiple reaction
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monitoring (MRM) mode. The creatinine concentration in each plasma sample was
determined by comparing the peak areas of the creatinine and D3-creatinine for the above
transitions. Blood urea nitrogen (BUN) was measured directly on IDEXX VetTest 8008
chemistry analyzer (Westbrook, Maine) using dry slide technology.
Analysis of atherosclerosis
At 24 weeks, mice were anesthetized, the thoracic cavity was exposed with a small incision
in the right cardiac auricle, and a cannula was inserted into the left ventricle. Through the
left ventricle, the animal was perfused with phosphate-buffered saline until the eluent from
the right auricle became clear, and then the left ventricle was injected with 3 ml of 10%
buffered formalin. Finally, the entire mice were immersed in the fixative at 4°C. Each aortic
tree was microdissected to remove adventitial fat and stained with Oil Red O (Sigma) to
visualize neutral lipids, pinned on wax plates. The images of the aorta were captured on a
digital camera. En face plaque quantification was performed with Image Pro software
(Media Cybernetics, Bethesda, MD). The lesional areas are represented as ratios between
surface area of atherosclerotic lesion stained with Oil Red O to the surface area of the entire
aortic tree (n=11, each group).
Arginine metabolome profiling by LC/MS
The detailed method development and chromatography optimization strategy for arginine
metabolomic profiling are discussed in Results. Targeted metabolomic analysis of arginine
metabolome in plasma was performed by LC/MS in the positive mode (n=11, each group).
Briefly, 20 μL of ethylenediaminetetraacetic acid anticoagulated plasma was spiked with D7
ADMA (10pM/sample), D6 SDMA (10pM/ sample), 13C6-arginine (600pM/sample)
and 13C5-citrulline (600pM/ sample). Protein was precipitated with 500μL acetonitrile. 5 μL
of the supernatant was subjected to HILIC with a Phenomenex Luna 3 μm, 2 × 150mm
column using an Agilent 1200 LC, at a flow rate of 300 μL/min. Solvent A was 10mM
ammonium formate and solvent B was acetonitrile with 0.1% formic acid. The column was
equilibrated with 95% solvent B and 5% solvent A initially. The gradient was: 95–15%
solvent B over 8 min, 15% solvent B for 6 min, 15–95% solvent B for 1 min and then finally
95% solvent B for 10 min.
The eluent from the LC was subjected MS analysis using an Agilent 6410 Triple
Quadrupole MS system) connected in series to the LC, equipped with an electrospray
source. Positive LC/ESI/MS was performed using following parameters: spray voltage 4000
V, drying gas flow 15 L/min, drying gas temperature 325°C, and nebulizer pressure 40 psi.
Flow injection analysis (FIA) using MS2 scan was used to optimize the fragmentor voltage
and collision energy for arginine, citrulline, NMMA, ADMA, and SDMA. To obtain the
best signal-to-noise ratio for quantitation, the most abundant ions from each compound were
chosen for use in MRM mode (Table 1). Limits of detection (LOD) were calculated using
peak areas corresponding to greater than five times signal to noise ratio. Data analysis was
performed with Agilent Mass Hunter Analyst software (Version B6, Agilent, Santa Clara
CA). In preliminary studies, we found that the correlation between the peak areas of labelled
standards and authentic compounds remained linear and greater than 0.95 irrespective of
internal standard concentrations being a log fold higher or lower than the authentic
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compound concentration for all the arginine metabolites. The amount of isotope labelled
standard spiked was subsequently individualized to each metabolite based on roughly 1:1
ratio of what is expected in the physiological range. Ratios of the peak area of metabolites to
the spiked isotope labelled compounds were used for quantification since the amount of
spike was known for each analyte that was measured. The most abundant fragment ion was
chosen as the MRM transitions for each metabolite (Table 1).
Statistical analysis
Results are given as mean ± SEM. Differences between the groups were considered
significant at p<0.05 using the program GraphPad Prism version 6.00 for Windows (La
Jolla, California, USA) for the independent t test when comparing characteristics of the
different groups. Pearson’s correlation was used to measure the strength of a linear
association between two variables using SPSS software for version 22 (SPSS Inc., Chicago,
Illinois). A two-sided p<0.05 was considered significant.
Results
Development of a targeted arginine metabolome profiling platform by LC/MS
Selection of column and LC conditions for optimal separation of the analytes—To select the most appropriate column and chromatography technique, we attempted
several different columns with both HILIC and reverse phase to optimize the separation. The
columns and LC conditions were based on previous literature. We first examined the
Phenomenex phenyl hexyl column (2.0 × 100mm, 3μm, Torrance, CA) with 10mM
ammonium formate as solvent A and methanol:H2O with 0.1% formic acid (1:3) as solvent
B at a flow rate of 0.25 mL/min. The column was equilibrated with 100% solvent A initially
and then decreased to 0% from 1–3 min, and then finally 100% solvent B from 3–5 min.
This resulted in very broad peak shapes. We then used the Agilent C18 reverse phase
column (2.1 × 50 mm, 1.8μm) with H2O + 0.1% formic acid as solvent A and acetonitrile
with 0.1% formic acid as solvent B at flow rate of 0.3 mL/min. The gradient was 100%
solvent A to begin with which was decreased to 0% at 6 min and 100% solvent B was
maintained between 4 min and 6 min. The peak areas were sharp but all compounds eluted
in the first column volume consistent with non-retention of the analytes to the column. We
then tested the Waters Symmetry C18 column (2.1 × 100 mm, 3.5μm, Milford, MA) with
10mM ammonium acetate as solvent A and 100% acetonitrile as solvent B at 0.3 mL/min
flow rate. The gradient began with 100% solvent A for 0.5 min, increased linearly between
0.5 min and 5 min to 100% solvent B and stayed at 100% solvent B between 5 and 6 min.
This method produced very poor peak shapes. While for the most part peak shapes were
acceptable, reverse phase separation resulted non-retention of the analytes as they were
eluted within either void or two-column volumes and therefore were sub-optimal. Finally,
we attempted Phenomenex HILIC Luna (3 μm, 2 × 150mm column (Torrance, CA)) at a
flow rate of 300 μL/min with solvent A 10mM ammonium formate and solvent B
acetonitrile with 0.1% formic acid. The column was equilibrated with 95% solvent B and
5% solvent A initially. The gradient was: 95–15% solvent B over 8 min, 15% solvent B for
6 min, 15–95% solvent B for 1 min and then finally 95% solvent B for 10 min. This method
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produced optimal peaks, retention times and shapes for ADMA, SDMA, NMMA, arginine
and citrulline. Therefore, this column and method was chosen for optimizing MS conditions.
Optimization of MS parameters—Using FIA of authentic standards, the fragmentor
voltage, cell acceleration voltage and collision energy was optimized for each individual
compound on an Agilent 6410 triple quadrupole MS. The molecular ion [M + H]+ of L-
arginine and 13C6 Arginine are m/z 175 and 181 respectively and the loss of m/z 105 yields
an intense ion at m/z 70 and 74 respectively. The molecular ion [M + H]+ of ADMA, D7
ADMA, SDMA and D6 SDMA are m/z 203, 210, 203 and 209 respectively. The loss of m/z
133 is the major fragment ion yielding product ions at m/z 70, 77, 70 and 76 respectively.
The molecular ion [M + H]+ of citrulline and 13C5 citrulline are m/z 176 and 181
respectively and the loss of m/z 17 yields fragments at m/z 159 and 164 respectively. These
transitions form ideal candidates for MRM analysis (Table 1). The limit of detection (LOD)
using this methodology was 64 fmol for arginine, 21 fmol for ADMA, 2 fmol for SDMA, 14
fmol for NMMA and 20 fmol for citrulline. The interassay and intra assay variability were 2
to 10% and less than 3% respectively for all compound to internal standard ratios.
Figure 2 depicts the extracted ion chromatogram (EIC) for the MRM transitions for ADMA
(Panel A), D7 ADMA(Panel B), SDMA (Panel C), D6SDMA (Panel D), NMMA (Panel E),
arginine (Panel F), 13C6 arginine (Panel G), citrulline (Panel H) and D5 citrulline (Panel I)
with the optimized HILIC separation. The MRM transitions noted in Figure 1 were utilized
for quantitative measurements of arginine metabolome in plasma.
CKD mouse model has biochemical evidence of CKD and increased atherosclerosis
The CKD mice at 24 weeks had significantly higher plasma creatinine (1.75 ± 0.54 vs. 0.97
± 0.34 mg/dL; n = 11; p<0.001) and BUN; (44.17 ± 1.79 vs. 28.25 ± 1.17 mg/dL; n = 11;
p<0.0001). The CKD mice had significantly lower body weight and hematocrit. The CKD
mice did not show significant differences in cholesterol levels, mineral metabolism
(calcium, phosphorus and intact parathyroid hormone) or blood pressure (Table 2). We
performed en face analysis of the entire aorta and stained with Oil Red O to determine lesion
area. Figure 3 Panel A and Panel B depict a representative control and CKD mouse aortic
arch following Oil Red O stain. The CKD mice had increased atherosclerotic lesion area
compared with control mice (0.16 ± 0.04 vs.0.06 ± 0.01; (n=9); p<0.05) (Figure 3: panel C;
n=11 per group). The data strongly support induction of CKD as a major factor that
accelerates atherosclerosis in this model.
Altered arginine methylation in CKD mice
Plasma ADMA, SDMA and NMMA levels were measured in plasma collected from the
control and CKD mice (n = 11 for each group). The plasma ADMA level was elevated in
CKD mice compared to control mice (0.35 ± 0.01 μM vs 0.28 ± 0.01 μM; p<0.01; Figure
4A). Similarly plasma SDMA was elevated in CKD mice compared to control mice (0.20 ±
0.01 μM vs.0.15 ± 0.01 μM; p<0.05; Figure 4B). The values for NMMA were not different
between the two groups (Figure 4C). Arginine methylation index, defined as the ratio of
dimethylated arginines to the monomethylated precursor (ADMA+SDMA)/NMMA) [36]
was significantly higher in the CKD mice, when compared to the control mice (29.55 ± 2.71
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vs. 20.64 ± 2.31 p<0.05; Figure 4D). Figure 4 panels E and F depict elevations of plasma
arginine (80.66 ± 7.03 μM vs. 62.63 ± 4.25 μM; p<0.05) and Citrulline ((15.07 ± 2.74 μM
vs. 5.79 ± 1.92 μM; p<0.05) in CKD mice compared to control mice. These data strongly
suggest that CKD alters arginine metabolism, raising the possibility that such alterations
may diminish NO bioavailability, contributing to vascular dysfunction.
Levels of altered arginine methylation products and their substrates correlate with each other
The ADMA levels of both control and CKD mice together correlate with SDMA levels (r =
0.549, p < 0.01) while both arginine and citrulline levels correlate with each other (r =
0.449, p < 0.05; Table 1) using pearson correlation. The substrate for ADMA production-
arginine and the by-product of ADMA degradation -citrulline levels do not correlate with
ADMA levels (arginine r=0.36 p=0.09; citrulline r=0.24; p=0.58; Table 3). This implies that
plasma citrulline is predominantly derived from arginine and not from ADMA. We also
performed Spearman correlation analysis between ADMA, SDMA, NMMA and arginine
and between citrulline and arginine which showed similar results (Data not shown).
ADMA levels, but not other arginine metabolites, are strongly associated with atherosclerotic burden
We tested whether arginine metabolites correlated with atherosclerotic burden by comparing
levels of the metabolites with lesional area, a measure of degree of atheroma in control and
CKD mice by performing Pearson Correlation analysis (Figure 3, panel B). Only ADMA
levels correlated with the lesional area (r = 0.64, p<0.01) while SDMA levels did not (r =
0.02, p>0.05). Similarly, NMMA and arginine methylation index did not correlate with
degree of atherosclerosis (Data not shown). The association of ADMA to atherosclerosis
was stronger in the CKD mice. This data strongly supports the notion that ADMA might
directly contribute to degree of atherosclerosis, perhaps by inhibiting eNOS and contributing
to decreased NO production.
Discussion
In this study, we utilized a mouse model of atherosclerosis to address whether alterations in
arginine metabolism, in part accounts for increased atherosclerosis observed in CKD. We
utilized LC/MS to profile and quantitatively measure arginine and its methylated derivatives
in plasma. We established that ADMA, SDMA, NMMA levels and the arginine methylation
index are elevated in CKD mice. ADMA levels are directly related to the extent of
atherosclerosis in CKD mice, suggesting a central role for altered NO bioavailability in
atheroma formation in this model. Finally, we provide evidence that the ADMA elevation is
not entirely related to availability of its substrate arginine or correlated to levels of its
byproduct citrulline but might be a result of potential enzymatic pathways that lead to
formation of methylated proteins or its degradation.
LC/ MS methodology for measurement of these methylated arginines remains the gold
standard for accurately measuring these methylated arginines. Arginine and its methylated
metabolites are extremely polar and are not well separated with standard reverse phase
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chromatography. Using HILIC, we are able to achieve optimal separation. Importantly, with
the use of tandem spectrometry we are able to measure levels of methylated arginines in the
low femtomolar range, which makes it highly sensitive especially for rodent studies where
sample availability is limiting. We used four isotopically labeled standards; D7 ADMA, D6
SDMA, 13C6 arginine and D5 citrulline to accurately determine the instrument response and
to account for ion suppression and matrix effects for each specific compound in contrast to
previously published methods that use one labeled standard for many compounds [37,38].
This method is the first to use labeled SDMA to accurately determine its concentration
independent of ADMA. Using the appropriate isotope labeled standard, this methodology
enables a very accurate measurement of the arginine metabolites with minimal interassay
and intraassay variability. This method also eliminates derivatization, solid phase extraction
and other complicated procedures like ultrafiltration and uses a straight forward protein
precipitation process, minimizing sample loss and expense.
The LDL receptor deficient mouse is a well-characterized model for atherosclerosis that
develops extensive lesions in aortic root and branches and perivascular system as described
previously [39]. The effect of CKD in this mouse model has also been described previously
and mainly results in acceleration of the lesions similar to non-CKD mice [40]. The mouse
however does not develop coronary atherosclerosis and hence we measured aortic root
lesion area as this is the most reproducible measurement. Following 5/6 nephrectomy, the
mice demonstrate features of CKD like increased BUN and creatinine. The mice do not have
increased blood pressure or changes in the calcium phosphorus metabolism, making CKD
the sole variable that could potentially contribute to atherosclerosis. The atherosclerosis in
this model is accelerated with high fat diet and similar models in the apoE −/− background
are previously reported [41].
Interestingly, a study by Jacobi et al reported no change in ADMA levels in subtotal
nephrectomized mice in apoE−/− background fed on chow diet for 12-months [42]. These
findings are in contrast to earlier publications that show increased ADMA levels in CKD
models and our current results [30,31]. Also surprisingly in that work, apoE−/− CKD mice
did not have higher atherosclerotic burden when compared with DDAH1 overexpressing
mice on an apoE−/− background. Over expression of DDAH1 in this mice model however
predictably decreased ADMA levels but did not change atherosclerotic lesions. It is likely
that these differences are attributable to strain differences, more modest reduction in renal
function and diet compared to our study. In our work, CKD mice have accelerated
atherosclerosis, manifested as increased luminal lipid accumulation, elevated aortic plaque,
necrotic core, fibrosis as well as greater luminal narrowing. Selective alterations of arginine
methylation were identified in our study and may in part account for propensity towards
increased atherosclerosis in this model. Indeed, the atherosclerotic burden correlated only
with ADMA levels, but not SDMA, NMMA or arginine methylation index.
CKD is associated with decreased NO bioavailability either due to decrease in production
due to substrate (arginine) limitation, increased levels of ADMA or increased utilization or
presence of increased vasoconstrictors [43]. We demonstrate in our work that changes in
ADMA in our model is not related to free arginine and citrulline in plasma suggesting that
ADMA levels are not entirely just a consequence of increased arginine levels due to
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decreased renal function. Elevated ADMA levels are probably a result of increased flux in
this pathway, partly due to increased methylation of proteins by PRMT, decreased renal
excretion and decreased DDAH1 activity. In a previous study, it was demonstrated that
ADMA levels are elevated in puromycin-induced CKD rats due to reduced DDAH activity
[15]. Studies in mongrel dogs have also revealed microvascular endothelial changes in CKD
dogs that was associated with increased ADMA levels and down regulation of DDAH-II
enzyme [44]. Activity changes in DDAH might be associated with loss-of-function
polymorphisms of a DDAH gene, functional inhibition of the enzyme by oxidative stress in
CKD and end-stage renal disease, or both [45]. DDAH1 overexpression in the apoE−/− mice
in a previous study demonstrated decreased plaque area in a non CKD model associated
with lower ADMA levels [46], but this has not been tested in CKD mice. Future studies will
need to focus on DDAH isoforms and the effect of DDAH overexpression in relevant CKD
models.
In clinical studies higher ADMA levels predicted cardiovascular events when compared to
control patients in pre-dialysis subjects [31,47,48]. In a large clinical study, Wang et al
demonstrated that arginine methylation index correlated with CVD outcomes [36].
Therefore, we tested the utility of this measurement in our CKD mouse model, but this
measure did not correlate with atherosclerotic burden, perhaps due to small numbers in our
study. ADMA together with related markers of oxidative stress like myeloperoxidase could
potentiate development of atherosclerosis [49]. Renal cyclooxygenase 2 (COX-2) inhibition
raises ADMA levels and could explain the increased cardiovascular morbidity of COX-2
inhibitors adding to ADMA’s role in cardiovascular disease [50]. Thus, ADMA together
with inflammatory and oxidative markers could play a central role in the accelerated
atherosclerotic burden in CKD.
Our model has several strengths. We demonstrate for the first time that in a
pathophysiologically relevant model of CKD atherosclerosis altered arginine methylation
and a high ADMA levels correlate with atherosclerotic burden. These changes occur even
with modest CKD suggesting that these pathways could be relevant in early CKD. The
mouse model does not have common associated features of CKD such as hypertension, and
abnormal mineral metabolism which make it an ideal model to study the effect of mild CKD
alone. The limitations of this work include small numbers in this study and lack of
manipulations to alter ADMA levels to show causality with atherosclerotic burden.
Manipulation of PRMT, DDAH or the alternate enzyme alanineglyoxalate
aminotransferase-2 (AGXT2) [51,52] could provide such direct evidence and future studies
need to focus on this issue. Finally, our findings raise the possibility that interrupting
arginine methylation pathways could provide a therapeutic avenue for combating CKD-
atherosclerosis.
Acknowledgments
Funding: The work is funded in part by Pilot and Feasibility Grant from the George O’Brien Kidney Center (DK081943). Mass spectrometry experiments were conducted in part at the Michigan Regional Comprehensive Metabolomics Resource and the Molecular Phenotyping Core, Michigan Nutrition and Obesity Center (DK089503 and DK097153).
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Abbreviations
ADMA Asymmetric Dimethyl Arginine (NG NG dimethylarginine)
BUN Blood Urea Nitrogen
CKD Chronic Kidney Disease
CAD Coronary Artery Disease
CV Cardiovascular
DDAH Dimethyl Arginine Dimethyl Amino Hydrolase
eNOS endothelial Nitric Oxide Synthetase
ESI Electrospray Ionization
EIC Extracted Ion Chromatogram
FIA Flow Injection Analysis
HILIC Hydrophilic Interaction Liquid Chromatography
LC/MS Liquid Chromatography/ Mass Spectrometry
LDLr−/− Low-density Lipoprotein Receptor Deficient
MRM Multiple Reaction Monitoring
NMMA NG Monomethyl-L-Arginine
NO Nitric Oxide
PRMT Protein Arginine Methyltransferases
SDMA Symmetric Dimethyl Arginine (NGN′G dimethylarginine)
References
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Figure 1. Arginine Metabolic PathwaysArginine residues in intact proteins are methylated by protein arginine methyl transferases
(PRMT) in to protein-bound NGmonomethyl-L-arginine (NMMA), NGNGdimethylarginine
(ADMA) and NGN′Gdimethylarginine (SDMA). Upon proteolysis, free NMMA, SDMA and
ADMA are released into circulation. Both ADMA and NMMA are inhibitors of endothelial
nitric oxide synthase (eNOS) - which synthesizes Nitric oxide – a potent vasoprotector from
free arginine. ADMA is degraded to citrulline by dimethylarginine dimethyl aminohydrolase
(DDAH) in the kidneys. The chemical structures of arginine metabolites are depicted.
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Figure 2. Detection of arginine metabolites by LC/MSExtracted ion chromatograms of ADMA (A), D7 ADMA(B), SDMA (C), D6 SDMA (D),
NMMA (E), arginine (F), 13C6- arginine (G), citrulline (H) and 13C5 citrulline (I) are shown.
Hydrophilic interaction chromatography (HILIC) of authentic compounds and their internal
standards was performed using Phenomenex HILIC column with solvent 10mM ammonium
acetate and 100% acetonitrile with 0.1% formic acid. The column was equilibrated with
95% solvent B and 5% solvent A initially. The gradient was: 95–15% solvent B over 8 min,
15% solvent B for 6 min, 15–95% solvent B for 1 min and then finally 95% solvent B for 10
min. The eluent was subjected electrospray ionization mass spectrometry (ESI/MS) and the
analytes were detected in the multiple reaction monitoring mode (MRM). X axis is denotes
time of elution during chromatographic run and the Y axis represents the ion intensity of the
extracted ion chromatogram of the daughter ion following ESI of the analyte in the MRM
mode. The MRM transitions that are followed for the individual analyte are depicted with
each parent molecular ion to daughter fragment that was monitored.
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Figure 3. Plasma ADMA correlates with atherosclerotic burden in CKD micePanel A and Panel B depict a representative en face analysis of control and CKD mouse
aortic tree following Oil Red O stain respectively. Lesion area was measured as described in
“Methods” and represented in Panel C for control and CKD mice (n =11 per group). Plasma
ADMA (Panel D) correlated with lesion area using Pearson correlation (*p<0.05).
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Figure 4. Plasma Arginine metabolome reveals changes in methylation in CKD miceADMA, SDMA, NMMA levels, arginine, citrulline and arginine methylation index (ARG
MI) were measured in plasma in control and CKD LDLr−/− mice by LC/MS. Bars represent
mean ± SEM (n= 11 each group). Plasma ADMA (A), SDMA (B), ARGMI (D), arginine
(E) and citrulline (F) are significantly higher in CKD mice. NMMA levels are not
significantly different (C) (*p<0.05).
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Table 1
MS spectral characteristics following collision induced disassociation (CID) of arginine metabolites.
Following ESI/MS in positive mode, MS spectrum was obtained for each arginine metabolite. The molecular
ion and major daughter ions following CID are represented.
Analyte Molecular ion (m/z) Daughter ions (m/z)
ADMA 203 46*, 70, 88, 116, 158
d6 ADMA 210 46*, 77, 71, 88, 165
SDMA 203 70*, 116, 88, 165
d5 SDMA 209 70*, 77, 116, 94, 60, 49, 30
NMMA 189 70*, 57
Arginine 175 70*, 60, 43
13C6 Arginine 181 74*, 61, 44, 74
Citrulline 176 159*,70,113
13C5 Citrulline 181 164*,75,118
*represents the most intense daughter ion that was chosen for MRM transition monitoring for quantification.
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Table 2
Physiological and Biochemical characteristics of study animals (n=11; each group)
CONTROL (n=11)Mean ± SD
CKD (n=11)Mean ± SD p value
Body weight (g) 33.52 ± 1.5 27.39 ± 0.6 <0.01
Blood Pressure (mm Hg) 102.70 ± 4.1 116.30 ± 9.2 NS
Hematocrit (%) 61.0 ± 0.9 52.91 ± 1.2 <0.0001
Serum Creatinine (mg/dL) 0.97 ± 0.34 1.75 ± 0.54 <0.001
Blood urea nitrogen (mg/dL) 28.25 ± 1.17 44.17 ± 1.79 <0.0001
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Tab
le 3
Pear
son
Cor
rela
tion
mat
rix
of th
e ar
gini
ne m
etab
olite
leve
ls o
f bo
th C
KD
and
con
trol
mic
e (n
=11
/ gro
up; t
otal
=22
).
AD
MA
SDM
AN
MM
AC
itru
lline
Arg
inin
e
AD
MA
Pear
son
Cor
rela
tion
1.5
49**
.290
.124
.361
Sig.
(2-
taile
d).0
08.1
90.5
82.0
99
SDM
APe
arso
n C
orre
latio
n.5
49**
1.0
17.1
58.0
73
Sig.
(2-
taile
d).0
08.9
42.4
82.7
48
NM
MA
Pear
son
Cor
rela
tion
.290
.017
1.1
06.3
38
Sig.
(2-
taile
d).1
90.9
42.6
40.1
23
Citr
ullin
ePe
arso
n C
orre
latio
n.1
24.1
58.1
061
.449
*
Sig.
(2-
taile
d).5
82.4
82.6
40.0
36
Arg
inin
ePe
arso
n C
orre
latio
n.3
61.0
73.3
38.4
49*
1
Sig.
(2-
taile
d).0
99.7
48.1
23.0
36
**C
orre
latio
n is
sig
nifi
cant
at t
he 0
.01
leve
l (2-
taile
d).
* Cor
rela
tion
is s
igni
fica
nt a
t the
0.0
5 le
vel (
2-ta
iled)
.
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